通过随机房间混响模型减少基于DNN的声学距离估计中的域失配
Diminishing Domain Mismatch for DNN-Based Acoustic Distance Estimation via Stochastic Room Reverberation Models
AI总结：
提出一种结合几何与随机建模RIR的新方法，减少基于DNN的声学距离估计中模拟与真实数据的域失配，显著提升估计精度。
摘要：
房间脉冲响应（RIR）编码了声源到传感器的距离等信息。深度神经网络（DNN）已被证明能够提取这些信息用于声学距离估计。由于带标注的数据（如带有距离信息的RIR）非常有限，训练用于声学距离估计的DNN必须依赖模拟的RIR，这导致与真实房间的RIR存在不可避免的失配。在本研究中，我们表明通过RIR的几何建模与随机建模的新颖组合，可以减少这种失配，从而显著提高距离估计的准确性。
英文摘要：
The room impulse response (RIR) encodes, among others, information about the distance of an acoustic source from the sensors. Deep neural networks (DNNs) have been shown to be able to extract that information for acoustic distance estimation. Since there exists only a very limited amount of annotated data, e.g., RIRs with distance information, training a DNN for acoustic distance estimation has to rely on simulated RIRs, resulting in an unavoidable mismatch to RIRs of real rooms. In this contribution, we show that this mismatch can be reduced by a novel combination of geometric and stochastic modeling of RIRs, resulting in a significantly improved distance estimation accuracy.
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